This protocol describes the steps needed to perform quantitative statistical colocalization on two-color confocal images, specifically of plant cells. The procedure includes a calibration test to check the chromatic alignment of the confocal microscope. A software tool is provided to calculate the Pearson and Spearman correlation coefficients ('Pearson-Spearman correlation colocalization' ImageJ plug-in) across regions of interest within the image. Steps are included to help the user practice using the software. The result is a quantitative estimate of the amount of colocalization in the images. Manual masking takes about 1-15 min per image, depending on the detail required, and calculating the correlation coefficients is almost instantaneous. Examples of suitable dyes for such two-color colocalization include Oregon Green or Alexa Fluor 488 dyes in the green range (excited with 488-nm laser line) and Alexa Fluor 555 dye in the red range (excited with 543-nm laser line).

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAAbased reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 908 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 408 to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.environmental sensing | systems biology R oot gravitropism has fascinated researchers since Knight (1) and Darwin (2). More recently, reorientation of Arabidopsis seedlings has been shown to trigger the asymmetric release of the growth regulator auxin from gravity-sensing columella cells at the root apex (Fig. 1A) (3-5). The resulting lateral auxin gradient is hypothesized to drive a differential growth response, where cell expansion on the lower side of the elongation zone is reduced relative to the upper side, causing the root to bend downward (6-8). Despite representing one of the oldest hypotheses in plant biology, key questions about auxin-regulated root gravitropism remain to be experimentally determined. How rapidly does the lateral auxin gradient form? Is this timescale consistent with the theory that auxin redistribution drives root bending? How long does the lateral auxin gradient persist? What triggers auxin redistribution to return to equal levels?Our understanding of gravity-induced auxin redistribution has been limited by the tools available to monitor auxin concentrations at high spatiotemporal resolution. Currently, the most widely used tools to follow auxin distribution in tissues are auxin-inducible reporters such as DR5::GFP (3, 4). However, as an output of the auxin response pathway (Fig. 1B), the activity of the DR5 reporter does not directly relate to endogenous auxin abundance, but also depends on additional parameters including local auxin signaling capacities and rates of transcription and translation (Fig. 1B). In practice, these intermediate processes confer a time delay of ∼1.5-2 h between changes in auxin abundance and DR5 reporter activity (9, 4), making it difficult to quantify the speed and magnitude of fold changes in auxin distribution during a root gravitropic response.Auxi...

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

Auxin is a key regulator of plant growth and development. Within the root tip, auxin distribution plays a crucial role specifying developmental zones and coordinating tropic responses. Determining how the organ-scale auxin pattern is regulated at the cellular scale is essential to understanding how these processes are controlled. In this study, we developed an auxin transport model based on actual root cell geometries and carrier subcellular localizations. We tested model predictions using the DII-VENUS auxin sensor in conjunction with state-of-the-art segmentation tools. Our study revealed that auxin efflux carriers alone cannot create the pattern of auxin distribution at the root tip and that AUX1/LAX influx carriers are also required. We observed that AUX1 in lateral root cap (LRC) and elongating epidermal cells greatly enhance auxin's shootward flux, with this flux being predominantly through the LRC, entering the epidermal cells only as they enter the elongation zone. We conclude that the nonpolar AUX1/LAX influx carriers control which tissues have high auxin levels, whereas the polar PIN carriers control the direction of auxin transport within these tissues.

We present a novel image analysis tool that allows the semiautomated quantification of complex root system architectures in a range of plant species grown and imaged in a variety of ways. The automatic component of RootNav takes a top-down approach, utilizing the powerful expectation maximization classification algorithm to examine regions of the input image, calculating the likelihood that given pixels correspond to roots. This information is used as the basis for an optimization approach to root detection and quantification, which effectively fits a root model to the image data. The resulting user experience is akin to defining routes on a motorist's satellite navigation system: RootNav makes an initial optimized estimate of paths from the seed point to root apices, and the user is able to easily and intuitively refine the results using a visual approach. The proposed method is evaluated on winter wheat (Triticum aestivum) images (and demonstrated on Arabidopsis [Arabidopsis thaliana], Brassica napus, and rice [Oryza sativa]), and results are compared with manual analysis. Four exemplar traits are calculated and show clear illustrative differences between some of the wheat accessions. RootNav, however, provides the structural information needed to support extraction of a wider variety of biologically relevant measures. A separate viewer tool is provided to recover a rich set of architectural traits from RootNav's core representation.